Biopsychosocial Compromise
ags
13-08-2020
Dataset Match
We used the Treatment Outcomes Profile to join users of the C1 (n= 109,756; users=85,048) that were evaluated with the Treatment Outcome Profile (TOP) in the same date of Admission to entries in C1 datasets.
###casos duplicados en fecha de aplicación
#1 1 97779
#2 2 2483
#3 3 149
#4 4 21
#5 5 3
#no hay casos que tengan más de una fecha de admisión Y UNA BD MÁS RECIENTE
#CONS_TOP_df_dup_ENE_2020_prev9%>% dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ap_top)))%>% group_by(concat) %>% dplyr::mutate(n_hash_fech_ap=n(),n_dis_ano=n_distinct(ano_bd))%>% dplyr::filter(n_hash_fech_ap>1,n_dis_ano>1)
invisible(
CONS_TOP_df_dup_ENE_2020_prev9%>% dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ap_top)))%>% group_by(concat) %>% dplyr::mutate(n_hash_fech_ap=n(),n_dis_ano=n_distinct(ano_bd))%>% dplyr::filter(n_hash_fech_ap>1,n_dis_ano>1)
)
CONS_TOP_df_dup_ENE_2020_prev9_match<-
CONS_TOP_df_dup_ENE_2020_prev9%>% dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ap_top)))%>% group_by(concat) %>% dplyr::mutate(n_hash_fech_ap=n(),n_dis_ano=n_distinct(ano_bd))%>% dplyr::filter(n_hash_fech_ap==1)%>%
dplyr::select(hash_key,concat,total_oh,dosis_oh,total_thc,dosis_thc,total_pbc,dosis_pbc,total_coc,dosis_coc,total_bzd,dosis_bzd,total_otra,dosis_otra,hurto,robo,venta_drogas,rina,total_vif,otro,total_transgresion,salud_psicologica,total_trabajo,total_educacion,salud_fisica,lugar_vivir,vivienda,calidad_vida,etapa_del_tratamiento)
CONS_C1_df_dup_JUL_2020_match_top<-
CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(compromiso_biopsicosocial), etapa_del_tratamiento=="Inicio Tratamiento")%>%
dplyr::select(-hash_key_TOP)%>%
dplyr::filter_at(vars(total_oh,dosis_oh,total_thc,dosis_thc,total_pbc,dosis_pbc,total_coc,dosis_coc,total_bzd,dosis_bzd,total_otra,dosis_otra,hurto,robo,venta_drogas,rina,total_vif,otro,total_transgresion,salud_psicologica,total_trabajo,total_educacion,salud_fisica,lugar_vivir,vivienda,calidad_vida,etapa_del_tratamiento), all_vars(!is.na(.)))
users_top_only_one_app_match<-CONS_TOP_df_dup_ENE_2020_prev9%>% dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ap_top)))%>% group_by(concat) %>% dplyr::mutate(n_hash_fech_ap=n(),n_dis_ano=n_distinct(ano_bd))%>% dplyr::filter(n_hash_fech_ap==1)%>% dplyr::ungroup()%>% dplyr::distinct(hash_key)%>% nrow()
#c("total_oh","dosis_oh","total_thc","dosis_thc","total_pbc","dosis_pbc","total_coc","dosis_coc","total_bzd","dosis_bzd","total_otra","dosis_otra","hurto","robo","venta_drogas","rina","total_vif","otro","total_transgresion","salud_psicologica","total_trabajo","total_educacion","salud_fisica","lugar_vivir","vivienda","calidad_vida")
# etapa_del_tratamiento n percent valid_percent
# Egreso 4 3.470114e-05 0.0005719188
# Inicio Tratamiento 6948 6.027587e-02 0.9934229339
# Seguimiento 12 meses 0 0.000000e+00 0.0000000000
# Seguimiento 15 meses 0 0.000000e+00 0.0000000000
# Seguimiento 3 meses 34 2.949597e-04 0.0048613097
# Seguimiento 6 meses 8 6.940227e-05 0.0011438376
# Seguimiento 9 meses 0 0.000000e+00 0.0000000000
# <NA> 108276 9.393251e-01 NAOf the total entries in TOP dataset (n= 109,756), we did not considered entries in TOP dataset had more than one application in the same date. We selected 6,375 cases that had no differences between the date of application and the date of admission to treatment. Additionally, these applications must had been administered at admission (excluding the 0,6% of applications that were administered at other stage of treatment). Finally, we discarded cases with null values in the variables of interest (see Figure 1).
Explore variables
We defined one outcome variable to indicate early drop-out (abandono_temp) that should vary depending on biopsychosocial compromise. We also collapsed variables related to transgression to norms (transg_norm) into a continuous variable, by counting actions committed in the last 4 weeks. Additionally, we added the variable salud_mean to get the mean of the scores in physical health, quality of life and psychological health. To capture the substance use behaviors, we defined the average pattern of drug use in alcohol, marijuana, cocaine paste base, cocaine sedatives and tranquilizers, and other substances, in terms of scores of quantity in the last four weeks (total_mean). Equally, we defined a score of the average dose of the mean reported by users on each substance. At last, we created the mean of scores of days of attendance to jobs or educational institutions in the last four weeks (trabajo_edu_mean). In case of total_vif_z, their distributions were very skewed, due to the fact that 79.8% had no scores, this is why we decided to recode it into absence of domestic violence (vif).
Values of doses over 50 were discarded and declared missing, due to the great amount of outliers.
Furthermore, we were interested in co-occurrent physical and psychiatric conditions among substance users. Consequently, we generated a variable called dg_cie_10, composed of users that at least had been diagnosed with a psychiatric condition or professionals had been studying a possible diagnosis. Also, we included dg_trs_fisico, which categorizes users without a diagnosed physical condition, and those that had one in study or had been diagnosed with a physical condition. Additionally, we generated the variable dg_cons_dep to identify drug dependence from hazardous consumption.
To capture profiles of consumption, we generated the following variables: oh identifies cases that used alcohol at least one day in the last four weeks. This same criteria was applied to marijuana (thc), cocaine paste base (pbc), cocaine (coc), benzodiazepines (bzd), and other drugs (otra). Users that reported at least two substances according to the aforementioned rule were categorized as polydrug users (polycons).
Lastly, the mean of the scores in many variables were dichotomized between users that had less than the 50% of users, compared to people that showed more scores than the 50% of the population. These were defined as Higher Frequency of Days using substances in the last four weeks (mayor_total), Higher Average Reported Daily Dose of Substances (mayor_dosis), Lower Health-Related Self-Reports (menor_salud), and Lower Days of Attendance to Work and Educational Centers in the last four weeks (menor_trabajo_edu). In a similar manner, transg_norm_bin identify users that reported at least one behavior that transgress the norm.
| Type | Variable | Missing | Complete Rate | Ordered | No. Unique | Top Counts | Mean (logical) | Counts (logical) | Mean | SD | Min | Perc. 25 | Median | Perc. 75 | Max | Histogram |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| factor | compromiso_biopsicosocial | 0 | 1.0000000 | FALSE | 3 | 2-M: 3380, 3-S: 1909, 1-L: 745 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | cie_10 | 0 | 1.0000000 | FALSE | 4 | Dia: 2374, Sin: 2357, En : 1185, Dia: 118 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | dg_trs_fisico | 0 | 1.0000000 | FALSE | 2 | 1: 4084, 0: 1950 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | abandono_temp | 0 | 1.0000000 | FALSE | 2 | 0: 4855, 1: 1179 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | origen_ingreso_mod | 0 | 1.0000000 | FALSE | 5 | Con: 3238, Sec: 1502, Der: 527, Sec: 486 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | motivodeegreso_mod_imp | 0 | 1.0000000 | FALSE | 5 | Aba: 2089, Alt: 1567, Aba: 1179, Der: 648 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | dg_trs_cons_sus_or | 0 | 1.0000000 | FALSE | 2 | Dep: 4252, Con: 1782 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | diagnostico_trs_fisico | 0 | 1.0000000 | FALSE | 23 | En : 3457, Sin: 1950, Otr: 159, Tra: 112 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | otros_probl_at_sm_or | 1,325 | 0.7804110 | FALSE | 12 | Sin: 2434, Vio: 1515, Otr: 603, Abu: 93 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | sus_principal_mod | 0 | 1.0000000 | FALSE | 5 | Alc: 2309, Pas: 2120, Coc: 1234, Mar: 296 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | otras_sus1_mod | 1,963 | 0.6746768 | FALSE | 5 | Alc: 1657, Mar: 1208, Coc: 703, Pas: 334 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | otras_sus2_mod | 3,852 | 0.3616175 | FALSE | 5 | Mar: 829, Alc: 638, Coc: 414, Pas: 156 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | otras_sus3_mod | 3,852 | 0.3616175 | FALSE | 5 | Mar: 829, Alc: 638, Coc: 414, Pas: 156 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | dg_trs_psiq_cie_10_or | 2,357 | 0.6093802 | FALSE | 12 | Tra: 1288, En : 1210, Tra: 532, Tra: 273 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | x2_dg_trs_psiq_cie_10_or | 5,797 | 0.0392774 | FALSE | 11 | En : 86, Tra: 55, Tra: 39, Tra: 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | x3_dg_trs_psiq_cie_10_or | 6,007 | 0.0044746 | FALSE | 9 | Tra: 9, En : 7, Tra: 5, Ret: 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | x4_dg_trs_psiq_cie_10_or | 6,032 | 0.0003315 | FALSE | 2 | Tra: 1, Tra: 1, En : 0, Ret: 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | x5_dg_trs_psiq_cie_10_or | 6,034 | 0.0000000 | FALSE | 0 | Tra: 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | alta_adm | 0 | 1.0000000 | FALSE | 2 | 0: 5483, 1: 551 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| factor | dg_cie_10_rec | 0 | 1.0000000 | FALSE | 2 | 1: 3677, 0: 2357 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | hurto | 0 | 1.0000000 | NA | NA | NA | 0.1093802 | FAL: 5374, TRU: 660 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | robo | 0 | 1.0000000 | NA | NA | NA | 0.0634736 | FAL: 5651, TRU: 383 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | venta_drogas | 0 | 1.0000000 | NA | NA | NA | 0.0447464 | FAL: 5764, TRU: 270 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | rina | 0 | 1.0000000 | NA | NA | NA | 0.1277759 | FAL: 5263, TRU: 771 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | otro | 0 | 1.0000000 | NA | NA | NA | 0.0331455 | FAL: 5834, TRU: 200 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | lugar_vivir | 0 | 1.0000000 | NA | NA | NA | 0.9191250 | TRU: 5546, FAL: 488 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | vivienda | 0 | 1.0000000 | NA | NA | NA | 0.9212794 | TRU: 5559, FAL: 475 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | vif | 0 | 1.0000000 | NA | NA | NA | 0.2053364 | FAL: 4795, TRU: 1239 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | na.rm | 0 | 1.0000000 | NA | NA | NA | 1.0000000 | TRU: 6034 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | transg_norm_bin | 0 | 1.0000000 | NA | NA | NA | 0.3619490 | FAL: 3850, TRU: 2184 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | menor_vivienda | 0 | 1.0000000 | NA | NA | NA | 0.1005966 | FAL: 5427, TRU: 607 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | menor_salud | 0 | 1.0000000 | NA | NA | NA | 0.4446470 | FAL: 3351, TRU: 2683 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | menor_trabajo_edu | 0 | 1.0000000 | NA | NA | NA | 0.5024859 | TRU: 3032, FAL: 3002 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | mayor_total | 0 | 1.0000000 | NA | NA | NA | 0.5122638 | TRU: 3091, FAL: 2943 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | mayor_dosis | 0 | 1.0000000 | NA | NA | NA | 0.5046404 | TRU: 3045, FAL: 2989 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | polycons | 0 | 1.0000000 | NA | NA | NA | 0.8967517 | TRU: 5411, FAL: 623 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | mayor_comp_biopsicsoc | 0 | 1.0000000 | NA | NA | NA | 0.3163739 | FAL: 4125, TRU: 1909 | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | menor_comp_biopsicsoc | 0 | 1.0000000 | NA | NA | NA | 0.1234670 | FAL: 5289, TRU: 745 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | total_oh | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.639211e+00 | 8.787051e+00 | 0.0000000 | 0.0000000 | 4.000000e+00 | 1.200000e+01 | 2.800000e+01 | <U+2587><U+2582><U+2582><U+2581><U+2582> |
| numeric | dosis_oh | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.529997e-01 | 2.115613e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 3.900000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_thc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.773782e+00 | 9.057474e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 4.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | dosis_thc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.529997e-01 | 2.115613e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 3.900000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_pbc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.217766e+00 | 9.243989e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 7.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | dosis_pbc | 6 | 0.9990056 | NA | NA | NA | NA | NA | 1.197412e+00 | 3.065544e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 4.000000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_coc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.750249e+00 | 6.207776e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 2.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | dosis_coc | 2 | 0.9996685 | NA | NA | NA | NA | NA | 2.780172e-01 | 1.804247e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 4.000000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_bzd | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.168711e-01 | 4.079368e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | dosis_bzd | 3 | 0.9995028 | NA | NA | NA | NA | NA | 4.209915e-01 | 2.703603e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 4.000000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_otra | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.479616e-01 | 4.865021e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | dosis_otra | 3 | 0.9995028 | NA | NA | NA | NA | NA | 4.209915e-01 | 2.703603e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 4.000000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_vif | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.370567e+00 | 4.525512e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_transgresion | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.785217e-01 | 7.973369e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 5.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | salud_psicologica | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.162081e+00 | 4.626610e+00 | 0.0000000 | 5.0000000 | 1.000000e+01 | 1.200000e+01 | 2.000000e+01 | <U+2583><U+2586><U+2587><U+2583><U+2581> |
| numeric | total_trabajo | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.087720e+01 | 1.053146e+01 | 0.0000000 | 0.0000000 | 1.000000e+01 | 2.000000e+01 | 2.800000e+01 | <U+2587><U+2581><U+2582><U+2583><U+2583> |
| numeric | total_educacion | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.215114e-01 | 2.302510e+00 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 2.800000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | salud_fisica | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.109032e+01 | 4.969154e+00 | 0.0000000 | 8.0000000 | 1.000000e+01 | 1.500000e+01 | 2.000000e+01 | <U+2582><U+2585><U+2587><U+2586><U+2583> |
| numeric | calidad_vida | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.035648e+01 | 5.104254e+00 | 0.0000000 | 7.0000000 | 1.000000e+01 | 1.500000e+01 | 2.000000e+01 | <U+2583><U+2585><U+2587><U+2585><U+2583> |
| numeric | sin_otros_prob_sm | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.033808e-01 | 4.906166e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2586> |
| numeric | dg_cons_dep | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.046735e-01 | 4.562272e-01 | 0.0000000 | 0.0000000 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2583><U+2581><U+2581><U+2581><U+2587> |
| numeric | cnt_mod_cie_10_or | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.044083e+00 | 2.289628e-01 | 1.0000000 | 1.0000000 | 1.000000e+00 | 1.000000e+00 | 4.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | row | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.124796e+05 | 2.002838e+04 | 74,802.0000000 | 95,657.2500000 | 1.123425e+05 | 1.283335e+05 | 1.618310e+05 | <U+2586><U+2587><U+2587><U+2586><U+2582> |
| numeric | transg_norm | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.838581e-01 | 9.605226e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 6.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | salud_mean | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.020296e+01 | 4.106428e+00 | 0.0000000 | 7.3333333 | 1.000000e+01 | 1.333333e+01 | 2.000000e+01 | <U+2582><U+2585><U+2587><U+2586><U+2582> |
| numeric | dosis_mean | 11 | 0.9981770 | NA | NA | NA | NA | NA | 6.685207e-01 | 1.371863e+00 | 0.0000000 | 0.0000000 | 1.666667e-01 | 8.333333e-01 | 1.900000e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_mean | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.690973e+00 | 3.363779e+00 | 0.0000000 | 1.0000000 | 3.000000e+00 | 5.333333e+00 | 2.333333e+01 | <U+2587><U+2583><U+2581><U+2581><U+2581> |
| numeric | vivienda_mean | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.601011e+00 | 1.255400e+00 | 0.0000000 | 5.0000000 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | <U+2581><U+2581><U+2581><U+2581><U+2587> |
| numeric | trabajo_edu_mean | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.599354e+00 | 5.327197e+00 | 0.0000000 | 0.0000000 | 5.000000e+00 | 1.000000e+01 | 2.400000e+01 | <U+2587><U+2582><U+2586><U+2581><U+2581> |
| numeric | salud_mean_z | 0 | 1.0000000 | NA | NA | NA | NA | NA | -1.158300e-02 | 9.970431e-01 | -2.4888676 | -0.7083302 | -6.086200e-02 | 7.484732e-01 | 2.367144e+00 | <U+2582><U+2585><U+2587><U+2586><U+2582> |
| numeric | dosis_mean_z | 11 | 0.9981770 | NA | NA | NA | NA | NA | -2.417700e-03 | 9.878153e-01 | -0.4837886 | -0.4837886 | -3.637796e-01 | 1.162564e-01 | 1.319724e+01 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_mean_z | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.238810e-02 | 1.002631e+00 | -1.0877693 | -0.7897023 | -1.935682e-01 | 5.019215e-01 | 5.867128e+00 | <U+2587><U+2582><U+2581><U+2581><U+2581> |
| numeric | vivienda_mean_z | 0 | 1.0000000 | NA | NA | NA | NA | NA | -8.369900e-03 | 1.014088e+00 | -3.7249801 | 0.3139260 | 3.139260e-01 | 3.139260e-01 | 3.139260e-01 | <U+2581><U+2581><U+2581><U+2581><U+2587> |
| numeric | total_vif_z | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.596800e-03 | 1.006895e+00 | -0.2993448 | -0.2993448 | -2.993448e-01 | -2.993448e-01 | 5.930462e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | total_trabajo_edu_z | 0 | 1.0000000 | NA | NA | NA | NA | NA | -6.085400e-03 | 1.000222e+00 | -1.0574064 | -1.0574064 | -1.186186e-01 | 8.201692e-01 | 3.448775e+00 | <U+2587><U+2582><U+2586><U+2581><U+2581> |
| numeric | oh | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.250580e-01 | 4.465220e-01 | 0.0000000 | 0.0000000 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2583><U+2581><U+2581><U+2581><U+2587> |
| numeric | thc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.457077e-01 | 4.756379e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2585> |
| numeric | pbc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.543255e-01 | 4.783481e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2585> |
| numeric | coc | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.099105e-01 | 4.624948e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2583> |
| numeric | bzd | 0 | 1.0000000 | NA | NA | NA | NA | NA | 6.629100e-02 | 2.488107e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | otra | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.408350e-02 | 2.052977e-01 | 0.0000000 | 0.0000000 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
#knitr::include_graphics("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/SUD_CL/Figures/Figure_Duplicates.svg")
#foreign::write.dta(CONS_C1_df_dup_JUL_2020_match_top_sel, "G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/c1_top.dta")
#CONS_C1_df_dup_JUL_2020 CONS_C1_df_dup_JUL_2020_match_top CONS_TOP_df_dup_ENE_2020_prev9_match CONS_TOP_df_dup_ENE_2020_prev9
#sólo match
comb_datasets_a_n<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(hash_key_TOP))%>%
dplyr::select(-hash_key_TOP)%>%
nrow()
comb_datasets_a_users<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(hash_key_TOP))%>%
dplyr::select(-hash_key_TOP)%>%
dplyr::distinct(hash_key)%>% nrow()
#sólo los que tienen compromiso biopsicosocial
comb_datasets_b_n<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(compromiso_biopsicosocial), !is.na(hash_key_TOP))%>%
dplyr::select(-hash_key_TOP)%>%
nrow()
comb_datasets_b_users<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(compromiso_biopsicosocial), !is.na(hash_key_TOP))%>%
dplyr::select(-hash_key_TOP)%>%
dplyr::distinct(hash_key)%>% nrow()
#sólo los que tienen etapa de tratamiento de inicio de tratamiento
comb_datasets_c_n<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(compromiso_biopsicosocial), !is.na(hash_key_TOP), etapa_del_tratamiento=="Inicio Tratamiento")%>%
dplyr::select(-hash_key_TOP)%>%
nrow()
comb_datasets_c_users<-CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(concat=paste0(hash_key,"_",as.character(fech_ing)))%>%
dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev9_match,by="concat", suffix=c("","_TOP"))%>%
dplyr::filter(!is.na(compromiso_biopsicosocial), !is.na(hash_key_TOP), etapa_del_tratamiento=="Inicio Tratamiento")%>%
dplyr::select(-hash_key_TOP)%>%
dplyr::distinct(hash_key)%>% nrow()
#sólo los que tienen tratamientos finalizados (no en curso)
# dplyr::filter(motivodeegreso_mod_imp!="En curso")%>% #Sacar los tratamientos que estén en curso
#https://stackoverflow.com/questions/46750364/diagrammer-and-graphviz
#https://mikeyharper.uk/flowcharts-in-r-using-diagrammer/
#http://blog.nguyenvq.com/blog/2012/05/29/better-decision-tree-graphics-for-rpart-via-party-and-partykit/
#http://blog.nguyenvq.com/blog/2014/01/17/skeleton-to-create-fast-automatic-tree-diagrams-using-r-and-graphviz/
#https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
#https://stackoverflow.com/questions/39133058/how-to-use-graphviz-graphs-in-diagrammer-for-r
#https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789802566/1/ch01lvl1sec21/creating-diagrams-via-the-diagrammer-package
#https://justlegal.be/2019/05/using-flowcharts-to-display-legal-procedures/
#
#
library(DiagrammeR) #⋉
grViz("digraph flowchart {
# node definitions with substituted label text
node [fontname = Helvetica, shape = rectangle,fontsize = 10]
tab1 [label = '@@1']
tab2 [label = '@@2']
tab3 [label = '@@3']
tab4 [label = '@@4']
tab5 [label = '@@5']
tab6 [label = '@@6']
tab7 [label = '@@7']
tab8 [label = '@@8']
blank [label = '', width = 0.001, height = 0.001]
# edge definitions with the node IDs
tab1 -> tab2;
blank -> tab3[label='',dir = none, shape=none, color = 'white',fontcolor = black, width=0, height=0];
{rank=same; tab2 -> tab3 [label='Left join',fontsize = 11, dir=none]}; #⋉
tab3 -> tab4 [label=' Result of the left join of the dataset',fontsize = 9];
tab4 -> tab5 [label=' Only rows with available data on Biopsychosocial Compromise',fontsize = 9];
tab5 -> tab6 [label=' Only strict matches with applications at the stage of admission',fontsize = 9];
tab6 -> tab7 [label=' Only rows with available data on TOP scores and Diagnostic of CIE-10',fontsize = 9];
tab7 -> tab8 [label=' Only rows with finished treatments',fontsize = 9];
}
Only rows with available data on TOP scores
[1]: paste0('TOP Dataset \\n(n = ', formatC(nrow(CONS_TOP_df_dup_ENE_2020_prev9), format='f', big.mark=',', digits=0), ';\\n users: ',formatC(CONS_TOP_df_dup_ENE_2020_prev9%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
[2]: paste0('Only applications w/ only one\\n application in the same date \\n(n = ', formatC(nrow(CONS_TOP_df_dup_ENE_2020_prev9_match), format='f', big.mark=',', digits=0), ';\\n users:',formatC(users_top_only_one_app_match, format='f', big.mark=',', digits=0),')')
[3]: paste0('Only applications w/ only one\\n application in the same date \\n(n = ', formatC(nrow(CONS_C1_df_dup_JUL_2020), format='f', big.mark=',', digits=0), ';\\n users:',formatC(CONS_C1_df_dup_JUL_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
[4]: paste0('Dataset \\n(n = ',formatC(comb_datasets_a_n,format='f', big.mark=',', digits=0),'\\nusers =',formatC(comb_datasets_a_users,format='f', big.mark=',', digits=0),')')
[5]: paste0('Dataset \\n(n = ',formatC(comb_datasets_b_n,format='f', big.mark=',', digits=0),'\\nusers =',formatC(comb_datasets_b_users,format='f', big.mark=',', digits=0),')')
[6]: paste0('Dataset \\n(n = ',formatC(comb_datasets_c_n,format='f', big.mark=',', digits=0),'\\nusers =',formatC(comb_datasets_c_users,format='f', big.mark=',', digits=0),')')
[7]: paste0('Dataset \\n(n = ', formatC(nrow(CONS_C1_df_dup_JUL_2020_match_top_sel_prev), format='f', big.mark=',', digits=0), ';\\n users: ',formatC(CONS_C1_df_dup_JUL_2020_match_top_sel_prev%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
[8]: paste0('Final Sample \\n(n = ', formatC(nrow(CONS_C1_df_dup_JUL_2020_match_top_sel), format='f', big.mark=',', digits=0), ';\\n users: ',formatC(CONS_C1_df_dup_JUL_2020_match_top_sel%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
")sdasd
#Definir la base de datos
CONS_C1_df_dup_JUL_2020_explore<-
CONS_C1_df_dup_JUL_2020%>%
dplyr::mutate(treat= ifelse(row %in% unlist(CONS_C1_df_dup_JUL_2020_match_top_sel$row),1,0))%>%
dplyr::mutate(abandono_temp=dplyr::case_when(motivodeegreso_mod_imp=="Abandono Temprano"~1,TRUE~0),
alta_adm= dplyr::case_when(grepl("trativa",as.character(motivodeegreso_mod_imp))~1,TRUE~0))%>%
dplyr::mutate(abandono_temp= as.factor(abandono_temp),alta_adm=as.factor(alta_adm),
origen_ingreso_mod=as.factor(origen_ingreso_mod))%>%
dplyr::mutate(dg_trs_fisico= dplyr::case_when(grepl("En estudio",as.character(diagnostico_trs_fisico))~1,
grepl("Sin trastorno",as.character(diagnostico_trs_fisico))~0,
is.na(as.character(diagnostico_trs_fisico))~0,
TRUE~1))%>%
dplyr::mutate(dg_cons_dep= dplyr::case_when(grepl("Dependencia",as.character(dg_trs_cons_sus_or))~1,
TRUE~0))%>%
dplyr::mutate(sin_otros_prob_sm= dplyr::case_when(grepl("Sin otros problemas de salud mental",as.character(otros_probl_at_sm_or), ignore.case=T)~1,
TRUE~0))%>%
dplyr::mutate(across(c(dg_trs_psiq_cie_10_or,x2_dg_trs_psiq_cie_10_or,x3_dg_trs_psiq_cie_10_or,x4_dg_trs_psiq_cie_10_or,x5_dg_trs_psiq_cie_10_or),~dplyr::case_when(grepl("En estudio",as.character(.))~0,grepl("Sin trastorno",as.character(.))~0,is.na(.)~0,TRUE~1), .names="dg_{col}"))%>%
dplyr::mutate(across(c(dg_trs_psiq_cie_10_or,x2_dg_trs_psiq_cie_10_or,x3_dg_trs_psiq_cie_10_or,x4_dg_trs_psiq_cie_10_or,x5_dg_trs_psiq_cie_10_or),~dplyr::case_when(grepl("En estudio",as.character(.))~1,grepl("Sin trastorno",as.character(.))~0,is.na(.)~0,TRUE~0), .names="instudy_{col}"))%>%
dplyr::mutate(instudy_total_cie_10 = base::rowSums(dplyr::select(., instudy_dg_trs_psiq_cie_10_or, instudy_x2_dg_trs_psiq_cie_10_or, instudy_x3_dg_trs_psiq_cie_10_or, instudy_x4_dg_trs_psiq_cie_10_or, instudy_x5_dg_trs_psiq_cie_10_or)))%>%
dplyr::mutate(dg_cie_10_rec=dplyr::case_when(grepl("Diagnosti",cie_10)~1,
grepl("En estudio",cie_10)~1,
TRUE~0))%>%
dplyr::mutate(dg_cie_10_rec=as.factor(dg_cie_10_rec))%>%
dplyr::mutate(dg_cie_10_rec=factor(dg_cie_10_rec,labels=c('No Disorder','In Study or Psychiatric Disorder')))%>%
dplyr::mutate(compromiso_biopsicosocial=factor(compromiso_biopsicosocial,labels=c('1-Mild','2-Moderate','3-Severe')))%>%
dplyr::mutate(compromiso_biopsicosocial=factor(origen_ingreso_mod,labels=c('Spontaneous','Assisted Referral','Other','Justice Sector','Health Sector')))%>%
dplyr::mutate(dg_trs_fisico=factor(dg_trs_fisico,labels=c('No Disorder','In Study or Physical Disorder')))%>%
dplyr::mutate(abandono_temp=factor(abandono_temp,labels=c('Other causes of discharge','Early Drop-out')))%>%
dplyr::mutate(dg_cons_dep=factor(dg_cons_dep,labels=c('No Drug Dependence','Drug Dependence')))%>%
dplyr::mutate(sin_otros_prob_sm=factor(sin_otros_prob_sm,labels=c('Other Mental Health Related Problems','No other mental health related problems')))%>%
dplyr::filter(!grepl("En curso", as.character(motivodeegreso_mod_imp), ignore.case = T))
label(CONS_C1_df_dup_JUL_2020_explore$dg_cie_10_rec) <- "Psychiatric Condition (ICD-10)"
label(CONS_C1_df_dup_JUL_2020_explore$sin_otros_prob_sm) <- "Other health-related problems"
label(CONS_C1_df_dup_JUL_2020_explore$dg_trs_fisico) <- "Physical Condition"
label(CONS_C1_df_dup_JUL_2020_explore$dg_cons_dep) <- "Drug Dependence"
label(CONS_C1_df_dup_JUL_2020_explore$abandono_temp) <- "Early Drop-Out"
label(CONS_C1_df_dup_JUL_2020_explore$compromiso_biopsicosocial) <- "Compromiso Biopsicosocial(d)/Biopsychosocial Involvement(d)"
label(CONS_C1_df_dup_JUL_2020_explore$origen_ingreso_mod) <-"Origen de Ingreso (Primera Entrada)/Motive of Admission to Treatment (First Entry)"
CONS_C1_df_dup_JUL_2020_concat <- dplyr::mutate(CONS_C1_df_dup_JUL_2020,concat=paste0(hash_key,"_",as.character(fech_ing)))
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
library(compareGroups)
table <- compareGroups(treat ~ compromiso_biopsicosocial+ origen_ingreso_mod+ abandono_temp+ dg_trs_fisico+ dg_cons_dep+ sin_otros_prob_sm+ dg_cie_10_rec, data = CONS_C1_df_dup_JUL_2020_explore,include.miss = T) #abandono_temp+ dg_cons_dep+ sin_otros_prob_sm dg_trs_fisico dg_cie_10+ alta_adm+Warning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'compromiso_biopsicosocial' are removed since no observation in
that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'origen_ingreso_mod' are removed since no observation in that/
those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'abandono_temp' are removed since no observation in that/those
levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'dg_trs_fisico' are removed since no observation in that/those
levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'dg_cons_dep' are removed since no observation in that/those
levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'sin_otros_prob_sm' are removed since no observation in that/
those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'dg_cie_10_rec' are removed since no observation in that/those
levelspvals <- getResults(table)
#p.adjust(pvals, method = "BH")
restab <- createTable(table)
export2md(restab, size=9,header.labels = c(p.overall = "p-value"), format="html",position="center",caption= "Table 2a. Summary descriptives table by groups, between selected rows (=1) and records that did not match (=0)")%>%
kableExtra::add_footnote(c(paste0("Note. Variables related to C1 dataset. Records of treatments not finished were omitted (n=",CONS_C1_df_dup_JUL_2020%>% dplyr::filter(grepl("En curso", as.character(motivodeegreso_mod_imp), ignore.case = T))%>% nrow(),")")), notation = "none") %>%
kableExtra::scroll_box(width = "100%", height = "375px")| 0 | 1 | p-value | |
|---|---|---|---|
| N=95876 | N=6034 | ||
| Compromiso Biopsicosocial(d)/Biopsychosocial Involvement(d): | <0.001 | ||
| Spontaneous | 43404 (45.3%) | 3238 (53.7%) | |
| Assisted Referral | 10601 (11.1%) | 527 (8.73%) | |
| Other | 4946 (5.16%) | 281 (4.66%) | |
| Justice Sector | 8467 (8.83%) | 486 (8.05%) | |
| Health Sector | 28458 (29.7%) | 1502 (24.9%) | |
| Origen de Ingreso (Primera Entrada)/Motive of Admission to Treatment (First Entry): | <0.001 | ||
| Consulta Espontánea | 43404 (45.3%) | 3238 (53.7%) | |
| Derivación Asistida | 10601 (11.1%) | 527 (8.73%) | |
| Otros | 4946 (5.16%) | 281 (4.66%) | |
| Sector Justicia | 8467 (8.83%) | 486 (8.05%) | |
| Sector Salud | 28458 (29.7%) | 1502 (24.9%) | |
| Early Drop-Out: | <0.001 | ||
| Other causes of discharge | 79105 (82.5%) | 4855 (80.5%) | |
| Early Drop-out | 16771 (17.5%) | 1179 (19.5%) | |
| Physical Condition: | <0.001 | ||
| No Disorder | 34879 (36.4%) | 1950 (32.3%) | |
| In Study or Physical Disorder | 60997 (63.6%) | 4084 (67.7%) | |
| Drug Dependence: | <0.001 | ||
| No Drug Dependence | 24025 (25.1%) | 1782 (29.5%) | |
| Drug Dependence | 71851 (74.9%) | 4252 (70.5%) | |
| Other health-related problems: | 0.020 | ||
| Other Mental Health Related Problems | 58648 (61.2%) | 3600 (59.7%) | |
| No other mental health related problems | 37228 (38.8%) | 2434 (40.3%) | |
| Psychiatric Condition (ICD-10): | <0.001 | ||
| No Disorder | 34632 (36.1%) | 2357 (39.1%) | |
| In Study or Psychiatric Disorder | 61244 (63.9%) | 3677 (60.9%) | |
| Note. Variables related to C1 dataset. Records of treatments not finished were omitted (n=7846) |
As seen in the table above, the treatments that did not matched with TOP datasets (users in treatment in where the TOP was not administered at the time of admission) may have slightly different characteristics than the sample with complete TOPs, excepting in the percentage of people that may had other health-related problems. This is relevant because this last variable is closely related to information assessed in the subsection of TOP named as transgression to norms.
vars_to_check <- c("hurto", "robo", "venta_drogas", "rina", "otro", "lugar_vivir", "vivienda", "salud_fisica", "calidad_vida", "salud_psicologica", "dosis_oh", "dosis_thc", "dosis_pbc", "dosis_coc", "dosis_bzd", "dosis_otra", "total_oh", "total_thc", "total_pbc", "total_coc", "total_bzd", "total_otra", "total_vif")
CONS_TOP_df_dup_ENE_2020_prev9_match_explore<-
CONS_TOP_df_dup_ENE_2020_prev9_match%>%
dplyr::left_join(CONS_C1_df_dup_JUL_2020_concat,by="concat", suffix=c("","_c1"))%>%
dplyr::ungroup()%>%
dplyr::filter(across(one_of(vars_to_check),
~ !is.na(.x)))%>%
dplyr::mutate(treat=ifelse(!is.na(hash_key_c1),1,0))%>%
dplyr::mutate(dosis_oh=ifelse(dosis_oh>=50, NA,dosis_oh))%>%
dplyr::mutate(dosis_oh=ifelse(dosis_thc>=50, NA,dosis_thc))%>%
dplyr::mutate(dosis_pbc=ifelse(dosis_pbc>=50, NA,dosis_pbc))%>%
dplyr::mutate(dosis_coc=ifelse(dosis_coc>=50, NA,dosis_coc))%>%
dplyr::mutate(dosis_bzd=ifelse(dosis_bzd>=50, NA,dosis_bzd))%>%
dplyr::mutate(dosis_otra=ifelse(dosis_otra>=50, NA,dosis_otra))%>%
dplyr::mutate_at(vars(hurto, robo, venta_drogas, rina, otro, lugar_vivir, vivienda), ~as.numeric(.)-1)%>%
dplyr::mutate(vif=ifelse(total_vif>0,1,0))%>%
dplyr::mutate(transg_norm = base::rowSums(dplyr::select(., hurto,robo,venta_drogas,rina,otro,vif), na.rm=T))%>%
dplyr::mutate(vif=as.logical(vif))%>%
dplyr::mutate(salud_mean = base::rowSums(dplyr::select(., salud_fisica, calidad_vida, salud_psicologica), na.rm=T)/3)%>%
dplyr::mutate(dosis_mean = base::rowSums(dplyr::select(., dosis_oh, dosis_thc, dosis_pbc, dosis_coc, dosis_bzd, dosis_otra), na.rm=T)/6)%>%
dplyr::mutate(total_mean = base::rowSums(dplyr::select(., total_oh, total_thc, total_pbc, total_coc, total_bzd, total_otra), na.rm=T)/6)%>%
dplyr::mutate(vivienda_mean = (base::rowSums(dplyr::select(.,lugar_vivir, vivienda), na.rm=T)/2)*5)%>%
dplyr::mutate(trabajo_edu_mean = base::rowSums(dplyr::select(., total_trabajo, total_educacion), na.rm=T)/2)%>%
dplyr::mutate_at(vars(hurto, robo, venta_drogas, rina, otro, lugar_vivir, vivienda), ~as.logical(.))%>%
dplyr::mutate(salud_mean_z=scale2(salud_mean,na.rm=T))%>%
dplyr::mutate(dosis_mean_z=scale2(dosis_mean,na.rm=T))%>%
dplyr::mutate(total_mean_z=scale2(total_mean,na.rm=T))%>%
dplyr::mutate(vivienda_mean_z=scale2(vivienda_mean,na.rm=T))%>%
dplyr::mutate(total_vif_z=scale2(total_vif,na.rm=T))%>%
dplyr::mutate(total_trabajo_edu_z=scale2(trabajo_edu_mean,na.rm=T))%>%
#dicotomizar variables
dplyr::mutate(oh=dplyr::case_when(total_oh>0~1,TRUE~0),
thc=dplyr::case_when(total_thc>0~1,TRUE~0),
pbc=dplyr::case_when(total_pbc>0~1,TRUE~0),
coc=dplyr::case_when(total_coc>0~1,TRUE~0),
bzd=dplyr::case_when(total_bzd>0~1,TRUE~0),
otra=dplyr::case_when(total_otra>0~1,TRUE~0))%>%
dplyr::mutate(transg_norm_bin=as.logical(dplyr::case_when(transg_norm>0~1,TRUE~0)))%>%
dplyr::mutate(median_vivienda_mean=quantile(vivienda_mean,p=.5,na.rm=T))%>%
dplyr::mutate(median_total_mean=quantile(total_mean,p=.5,na.rm=T))%>%
dplyr::mutate(median_dosis_mean=quantile(dosis_mean,p=.5,na.rm=T))%>%
dplyr::mutate(median_salud_mean=quantile(salud_mean,p=.5,na.rm=T))%>%
dplyr::mutate(median_trabajo_edu_mean=quantile(trabajo_edu_mean,p=.5,na.rm=T))%>%
dplyr::mutate(menor_vivienda=dplyr::case_when(vivienda_mean<median_vivienda_mean~1,TRUE~0))%>%
dplyr::mutate(menor_salud=dplyr::case_when(salud_mean<median_salud_mean~1,TRUE~0))%>%
dplyr::mutate(menor_trabajo_edu=dplyr::case_when(trabajo_edu_mean<median_trabajo_edu_mean~1,TRUE~0))%>%
dplyr::mutate(mayor_total=dplyr::case_when(total_mean>=median_total_mean~1,TRUE~0))%>%
dplyr::mutate(mayor_dosis=dplyr::case_when(median_dosis_mean>=dosis_mean~1,TRUE~0))%>%
dplyr::mutate_at(vars(menor_salud, menor_trabajo_edu, mayor_total, mayor_dosis), ~as.logical(.))%>%
dplyr::mutate(polycons = base::rowSums(dplyr::select(., oh, thc, pbc, coc, bzd, otra)),
polycons =as.logical(ifelse(polycons>0,1,0)))%>%
dplyr::mutate(mayor_dosis=dplyr::case_when(median_dosis_mean>=dosis_mean~1,TRUE~0))%>%
dplyr::mutate(mayor_dosis=as.logical(mayor_dosis))%>%
dplyr::mutate(menor_vivienda=as.logical(menor_vivienda))
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$salud_mean_z) <- "Promedio ptjes. est. de estado de salud autorreportado/Average std. score of self-reported health"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$dosis_mean_z) <- "Promedio ptjes. est. de dosis media/Average std. score of average dose"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$total_mean_z) <- "Promedio ptjes. est. de patrones de uso de sus. principales/Average std. median score of patterns of drug use in main substances"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$total_trabajo_edu_z) <- "Promedio est. de días trabajados o asistidos a una institución educacional/Average std. days worked and attended an educational institution"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$total_vif_z) <- "Ptjes. est. del Total en Violencia Intrafamiliar/std. score in Domestic Violence"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$hurto) <- "Hurto/Theft (Transgression to Norms in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$robo) <- "Robo/Robbery (Transgression to Norms in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$venta_drogas) <- "Venta de Drogas/Drug-selling (Transgression to Norms in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$rina) <- "Riña/Fights (Transgression to Norms in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$otro) <- "Otras Transgresiones a la Norma/Other transgression to norms (Transgression to Norms in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$lugar_vivir) <- "Lugar Estable para Vivir/Stable Place to Live in the past 4 weeks (Housing conditions in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$vivienda) <- "Habita en una vivienda con las condiciones básicas/Lived in a Houshold with basic conditions in the past 4 weeks (Housing conditions in past 4 weeks)"
label(CONS_TOP_df_dup_ENE_2020_prev9_match_explore$vif) <- "Violencia Intrafamiliar Reportada en las últimas 4 semanas/Reported Domestic Violence Events in the past 4 weeks (Transgression to Norms)"
table <- compareGroups(treat ~salud_mean_z+ dosis_mean_z+ total_mean_z+ total_trabajo_edu_z+ hurto+ robo+ venta_drogas+ rina+ otro+ lugar_vivir+vif+ vivienda,
data = CONS_TOP_df_dup_ENE_2020_prev9_match_explore,include.miss = T,var.equal=T,method=c(2,2,2,2,3,3,3,3,3,3,3,3))Warning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'hurto' are removed since no observation in that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'robo' are removed since no observation in that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'venta_drogas' are removed since no observation in that/those
levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'rina' are removed since no observation in that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'otro' are removed since no observation in that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'lugar_vivir' are removed since no observation in that/those
levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'vif' are removed since no observation in that/those levelsWarning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'vivienda' are removed since no observation in that/those levelspvals <- getResults(table, "p.overall")
#p.adjust(pvals, method = "BH")
restab <- createTable(table)
export2md(restab, header.labels = c(p.overall = "p-value"), size=9,format="html",position="center",caption= "Table 2b. Summary descriptives table by groups, between selected rows (=1) and records that did not match (=0)")%>%
kableExtra::add_footnote(c("Note. Variables related to TOP dataset.", paste0("We discarded cases with missing values in one of the variables (n=", CONS_TOP_df_dup_ENE_2020_prev9_match%>%dplyr::left_join(CONS_C1_df_dup_JUL_2020_concat,by="concat", suffix=c("","_c1"))%>%dplyr::ungroup()%>%dplyr::filter(across(one_of(vars_to_check),~ is.na(.x)))%>% nrow(),")")), notation = "none") %>%
kableExtra::scroll_box(width = "100%", height = "375px")| 0 | 1 | p-value | |
|---|---|---|---|
| N=86400 | N=6507 | ||
| Promedio ptjes. est. de estado de salud autorreportado/Average std. score of self-reported health | 0.11 [-0.68;0.82] | -0.68 [-1.31;0.11] | 0.000 |
| Promedio ptjes. est. de dosis media/Average std. score of average dose | -0.36 [-0.36;-0.07] | -0.21 [-0.36;0.22] | <0.001 |
| Promedio ptjes. est. de patrones de uso de sus. principales/Average std. median score of patterns of drug use in main substances | -0.54 [-0.66;0.23] | 0.35 [-0.36;1.24] | 0.000 |
| Promedio est. de días trabajados o asistidos a una institución educacional/Average std. days worked and attended an educational institution | -0.10 [-1.02;0.82] | -0.10 [-1.02;0.82] | 0.103 |
| Hurto/Theft (Transgression to Norms in past 4 weeks): | <0.001 | ||
| FALSE | 82209 (95.1%) | 5812 (89.3%) | |
| TRUE | 4191 (4.85%) | 695 (10.7%) | |
| Robo/Robbery (Transgression to Norms in past 4 weeks): | <0.001 | ||
| FALSE | 83979 (97.2%) | 6108 (93.9%) | |
| TRUE | 2421 (2.80%) | 399 (6.13%) | |
| Venta de Drogas/Drug-selling (Transgression to Norms in past 4 weeks): | <0.001 | ||
| FALSE | 84599 (97.9%) | 6217 (95.5%) | |
| TRUE | 1801 (2.08%) | 290 (4.46%) | |
| Riña/Fights (Transgression to Norms in past 4 weeks): | <0.001 | ||
| FALSE | 81307 (94.1%) | 5691 (87.5%) | |
| TRUE | 5093 (5.89%) | 816 (12.5%) | |
| Otras Transgresiones a la Norma/Other transgression to norms (Transgression to Norms in past 4 weeks): | <0.001 | ||
| FALSE | 84154 (97.4%) | 6284 (96.6%) | |
| TRUE | 2246 (2.60%) | 223 (3.43%) | |
| Lugar Estable para Vivir/Stable Place to Live in the past 4 weeks (Housing conditions in past 4 weeks): | <0.001 | ||
| FALSE | 5627 (6.51%) | 519 (7.98%) | |
| TRUE | 80773 (93.5%) | 5988 (92.0%) | |
| Violencia Intrafamiliar Reportada en las últimas 4 semanas/Reported Domestic Violence Events in the past 4 weeks (Transgression to Norms): | <0.001 | ||
| FALSE | 77268 (89.4%) | 5192 (79.8%) | |
| TRUE | 9132 (10.6%) | 1315 (20.2%) | |
| Habita en una vivienda con las condiciones básicas/Lived in a Houshold with basic conditions in the past 4 weeks (Housing conditions in past 4 weeks): | 0.072 | ||
| FALSE | 6195 (7.17%) | 506 (7.78%) | |
| TRUE | 80205 (92.8%) | 6001 (92.2%) | |
| Note. Variables related to TOP dataset. | |||
| We discarded cases with missing values in one of the variables (n=244) |
In the Table above and despite we were focused in the treatments of the Agreement 1 dataset, we may see that the only variable that did not have greater differences between the matched records and those that did not is the basic housing conditions and the standardized average days worked or assisted to educational institutions. The rest may vary greatly.
To get an idea of the distribution of the biopsychosocial compromise among the selected sample, we generated a pie chart.
library(plotly)
CONS_C1_df_dup_JUL_2020_match_top_sel %>%
dplyr::group_by(compromiso_biopsicosocial)%>%
dplyr::mutate(compromiso_biopsicosocial=case_when(compromiso_biopsicosocial=="2-Moderado"~"Moderate",
compromiso_biopsicosocial=="1-Leve"~"Mild",
TRUE~"Severe"))%>%
dplyr::summarise(n=n(),percent=round(n/sum(n),2))%>%
dplyr::ungroup()%>%
plot_ly( labels = ~compromiso_biopsicosocial, values = ~n, type = 'pie',hole = 0.6,textposition = 'outside',textinfo = 'label+percent', marker = list(colors = c("yellowgreen","orange","tomato"))) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))Figure 2. Biopsychosocial Compromise
We generated a correlation plot which wraps different type of variables (continuous, polytomous, dichotomous).1 We selected mostly the original variables from the instrument.
# It first identifies the types of variables, organizes them by type (continuous, polytomous, dichotomous), calls the appropriate correlation function, and then binds the resulting matrices together.D
mix_cor<- psych::mixedCor(data=CONS_C1_df_dup_JUL_2020_match_top_sel%>%dplyr::mutate(compromiso_biopsicosocial=as.numeric(compromiso_biopsicosocial)),c=2:20, #total_oh,dosis_oh total_thc dosis_thc total_pbc dosis_pbc total_coc dosis_coc total_bzd dosis_bzd total_otra dosis_otra total_vif total_transgresion salud_psicologica total_trabajo total_educacion salud_fisica calidad_vida
p=1,#compromiso_biopsicosocial,
d=21:27,#, hurto robo venta_drogas rina otro lugar_vivir vivienda
smooth=TRUE,
correct=.5,
global=TRUE,
ncat=8,
use="pairwise",
method="kendall",
weight=NULL)# Getting the correlation matrix of the dataset.
comp_biopsicosoc_mat <- mix_cor$rho
# Explore the correlation structure of the dataset.
library(ggcorrplot)
ggcorrplot(comp_biopsicosoc_mat,
ggtheme = ggplot2::theme_gray,
colors = c("#E46726", "white", "#6D9EC1"),
tl.cex=8)
Figure 3.Matrix Of Correlations Between Original Variables
#subscript out of bounds
#mixedCor(data=CONS_C1_df_dup_JUL_2020_match_top_sel,c=c("total_oh","dosis_oh", "total_thc", "dosis_thc", "total_pbc", "dosis_pbc", "total_coc", "dosis_coc", "total_bzd", "dosis_bzd", "total_otra", "dosis_otra", "total_vif", "total_transgresion", "salud_psicologica", "total_trabajo", "total_educacion", "salud_fisica", "calidad_vida"), p="compromiso_biopsicosocial",d=c("hurto", "robo", "venta_drogas", "rina", "otro", "lugar_vivir", "vivienda"), smooth=TRUE,correct=.5,global=TRUE,ncat=8,use="pairwise",method="pearson",weight=NULL)
#subscript out of bounds
#set.seed(123)
#par(mar=c(1,1,1,1))
#comp_biopsicosoc_mat_ci<-mycor.ci(x=CONS_C1_df_dup_JUL_2020_match_top_sel%>%dplyr::mutate(compromiso_biopsicosocial=as.numeric(compromiso_biopsicoso#cial)),
# method="spearman",
# poly=TRUE,
# cvars=2:20,
# pvars=1,
# dvars=21:27,
# n.iter=1000,
# plot = F)
#
#cor_pmat(): para ver la sig estadística de las correlacionesAlso we included an heterogeneus matrix of correlations, consisting of polychoric, biserial and pearson correlations.2 In the following matrix, we selected the recoded variables useful to contrast our hypotheses.
require(polycor)
#Computes a heterogenous correlation matrix, consisting of Pearson product-moment correlations between numeric variables, polyserial correlations between numeric and ordinal variables, and polychoric correlations between ordinal variables.
hetcor_mat<-hetcor(CONS_C1_df_dup_JUL_2020_match_top_sel[c("compromiso_biopsicosocial",
"dosis_mean_z","total_mean_z","salud_mean_z","total_trabajo_edu_z","hurto","robo","venta_drogas","rina","otro","lugar_vivir","vivienda","vif",
"dg_cons_dep","sin_otros_prob_sm","abandono_temp","dg_cie_10_rec")], ML = T, std.err = T, use="complete.obs", bins=4, pd=TRUE)
#CONS_C1_df_dup_JUL_2020_match_top_sel%>% select(contains("dosis"))%>% hist()
ggcorrplot(hetcor_mat$correlations,
ggtheme = ggplot2::theme_gray,
colors = c("#E46726", "white", "#6D9EC1"), tl.cex=8)
Figure 4.Matrix Of Correlations Between Transformed Variables
Variables of TOP depending on Biopsychosocial Compromise
library(reshape2) # for melt() function
Attaching package: 'reshape2'The following objects are masked from 'package:data.table':
dcast, meltThe following object is masked from 'package:tidyr':
smithslibrary(ggplot2)
# First we need to restructure the data into long format:
ocdMelt <- melt(CONS_C1_df_dup_JUL_2020_match_top_sel[c('compromiso_biopsicosocial','hurto',"robo","venta_drogas","rina","otro","lugar_vivir","vivienda","vif")], id=c('compromiso_biopsicosocial'))
names(ocdMelt) <- c('Group', 'Outcome_Measure', 'Frequency')
ocdMelt <-ocdMelt%>% dplyr::mutate(Frequency=factor(Frequency))%>% dplyr::group_by(Outcome_Measure,Group,Frequency)%>% dplyr::arrange(Outcome_Measure, Group,Frequency)%>% dplyr::add_tally()%>% distinct(.,.keep_all=T)%>% dplyr::ungroup()%>% dplyr::group_by(Group, Outcome_Measure)%>% dplyr::mutate(perc=n/sum(n))%>% dplyr::ungroup()
# plot
p5<-ggplot(ocdMelt, aes(Outcome_Measure,perc))+
geom_bar(aes(fill= Frequency),stat="identity")+
facet_grid(Group~.)+
scale_y_continuous(labels = scales::percent_format(accuracy = 5L),limits=c(0,1))+
scale_fill_manual(values=c("grey60","steelblue"),labels=c("Absence", "Presence")) +
sjPlot::theme_sjplot2() +
ylab("Biopsychosocial Compromise")+
theme(axis.text.x = element_text(size=8,vjust = .5,angle=25), axis.text.y = element_text(size=10),
axis.title.y = element_text(size = 14)) +
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
line = element_blank())
p5
Figure 5. Bar Plot of Binary Answers By Biopsychosocial Compromise
We generated z scores for those composite scores of multiple variables, in order to make them comparable. In the following figure we may observe the distribution of continuous variables among users with different levels of biopsychosocial compromise.
library(reshape2) # for melt() function
library(ggplot2)
# First we need to restructure the data into long format:
ocdMelt <- melt(CONS_C1_df_dup_JUL_2020_match_top_sel[c('compromiso_biopsicosocial',"salud_mean_z","dosis_mean_z", "total_mean_z","total_trabajo_edu_z")], id=c('compromiso_biopsicosocial'))
names(ocdMelt) <- c('Group', 'Outcome_Measure', 'Frequency')
# plot
ocdBoxplot <- ggplot(ocdMelt, aes(Group, Frequency, color = Outcome_Measure)) +
geom_jitter(alpha=0.05) +
geom_boxplot() +
labs(x='Biopsychosocial Compromise', y='Scores', color='Variable') + scale_y_continuous(breaks=seq(0,50, by=5))+
sjPlot::theme_sjplot2() +
scale_color_manual(values=c("darkslategray4","rosybrown","dimgray","coral3","grey60"),na.value="black") + #"Set2"
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
line = element_blank())
ocdBoxplotWarning: Removed 11 rows containing non-finite values (stat_boxplot).Warning: Removed 11 rows containing missing values (geom_point).
Figure 6. Box Plot of Continuous Answers By Biopsychosocial Compromise
As seen above, users categorized with a severe Biopsychosocial Compromise showed slightly greater patterns of consumption and lower average health in comparison to users with a mild Biopsychosocial Compromise. However, and at least in an descriptive view, they seem to not differ much between the different levels.
We checked the multivariate normality of these four last variables, in order to choose the most adequate technique to perform the analyses. These analyses may let us infer whether that users in different levels of Biopsychosocial Compromise derive from the same population in terms of scores in the Treatment Outcomes Profile.
| Test | Statistic | P Value | Normality |
|---|---|---|---|
| Mardia Skewness | 26,903.493 | 0.000 | NO |
| Mardia Kurtosis | 218.874 | 0.000 | NO |
| MVN | NA | NA | NO |
| Note. Using z-scores distributions of Average Health, Average days worked or in assistance to educational institutions, Average Frequency of Use in the last Month and Average Dose in the last Month |
As seen above, the continuous variables did not follow a normal distribution.
Non-parametric Multivariate Test
Considering that our variables did not follow a normal distribution, and the test is composed by a mixture of different variable types, we performed a nonparametric test for multivariate samples. The variability of responses on each of the variables of top was compared between the groups using a non-parametric multivariate ANOVA (mnpv)3 with biopsychosocial compromise as between-subject factor (discarding inter-observer variability). The procedure generates a randomization test of 5,000 resamples to compute F-statistic (pseudo-F). Additionally, these models have the advantage that allow for differences in between-group variation, is not sensitive to multicollinearity, allows for the inclusion of more variables, and tolerate sparse data.
#mvnormtest::mshapiro.test()
dependent.vars <- cbind(CONS_C1_df_dup_JUL_2020_match_top_sel$salud_mean_z,CONS_C1_df_dup_JUL_2020_match_top_sel$dosis_mean_z, CONS_C1_df_dup_JUL_2020_match_top_sel$total_mean_z)
root.manova <- manova(dependent.vars ~ CONS_C1_df_dup_JUL_2020_match_top_sel$compromiso_biopsicosocial, intercept=F)
summary(root.manova,test="Pillai")
#test='Pillai'). The manova() function provides four multivariate tests by setting the test argument to either Pillai, #Wilks, Hotelling-Lawley, or Roy.
#La prueba de Wilk es la más comúnmente utilizada debido a que fue la primera prueba en ser derivada y a que posee una aproximación F bastante conocida.
#Lawley-Hotelling es también conocida como el estadístico T2 generalizado de Hotelling.
#La prueba de Pillai es la mejor prueba a utilizar en la mayoría de las situaciones. La prueba de Pillai proporciona resultados similares a los de las pruebas de Wilks y de Lawley-Hotelling.
#La prueba de la raíz más grande de Roy es la mejor cuando los vectores medios son colineales. La prueba de Roy no tiene una aproximación F satisfactoria.
summary.aov(root.manova)CONS_C1_df_dup_JUL_2020_match_top_sel2<-
CONS_C1_df_dup_JUL_2020_match_top_sel%>%
dplyr::mutate_at(vars(hurto, robo, venta_drogas, rina, otro, lugar_vivir, vivienda,vif), ~as.numeric(as.factor(as.numeric(.)-1)))
#https://stackoverflow.com/questions/59166335/need-help-using-npmv-package-nonparametric-testing-in-r
invisible(
prueba_anormalidad1<-npmv::nonpartest(dosis_mean_z|total_mean_z|salud_mean_z|total_trabajo_edu_z|hurto|robo|venta_drogas|rina|otro|lugar_vivir|vivienda|vif~compromiso_biopsicosocial, CONS_C1_df_dup_JUL_2020_match_top_sel2,plots=F,permreps=5000,releffects=TRUE)
)
prueba_anormalidad_res1<-data.table::data.table(prueba_anormalidad1$results, keep.rownames=T)
#INOCRPORË LAS VAR DE CONTROL. SON TODAS DE TIPO FACTOR
sum_df_npmv<-data.frame(cbind(Model=paste0("npmv",c("Raw","Raw","Raw","Raw")),
rbind(prueba_anormalidad_res1)))%>%
dplyr::rename("Parameter"="rn")%>%
dplyr::mutate(`P.value`=sprintf("%5.3f",P.value), `Permutation.Test.p.value`=sprintf("%5.3f",Permutation.Test.p.value))
sum_df_npmv%>%
dplyr::mutate(Test.Statistic=round(as.numeric(as.character(Test.Statistic)),3))%>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Table 4. Analysis of Variance",
align =rep('c', 101),
col.names=c("Model", "Parameter", "Statistic", "DF1", "DF2","p-value", "Permutation Test p-value"))%>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::add_footnote( c("Note. Model with average dose (z score), average frequency of use (z score), average reported health (z score), average days worked or attending an educational institution (z score), theft, burglary, drug selling, fights, other, place to live, housing condition and domestic violence."), notation = "none") %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Model | Parameter | Statistic | DF1 | DF2 | p-value | Permutation Test p-value |
|---|---|---|---|---|---|---|
| npmvRaw | ANOVA type test p-value | 176.234 | 12.152 | 25,236.39 | 0.000 | 0.000 |
| npmvRaw | McKeon approx. for the Lawley Hotelling Test | 57.292 | 24.000 | 10,411.40 | 0.000 | 0.000 |
| npmvRaw | Muller approx. for the Bartlett-Nanda-Pillai Test | 52.297 | 24.012 | 12,023.99 | 0.000 | 0.000 |
| npmvRaw | Wilks Lambda | 54.793 | 24.000 | 12,018.00 | 0.000 | 0.000 |
| Note. Model with average dose (z score), average frequency of use (z score), average reported health (z score), average days worked or attending an educational institution (z score), theft, burglary, drug selling, fights, other, place to live, housing condition and domestic violence. |
##NO OCUPAR: MUY COMPLICADO
#The function ssnonpartest performs an all-subset algorithm to determine which variables cause significant effects, and between which factor levels. See the examples below for some basic uses and look in the help pages for each function for a much more detailed look.
#invisible(
#npmv::ssnonpartest(dosis_mean|total_mean|trabajo_edu_mean~compromiso_biopsicosocial,CONS_C1_df_dup_JUL_2020_match_top_sel,test=c(1,0,0,0),alpha=.05,factors.and.variables=TRUE)
#)We also included the non-parametric relative treatment efects (RTE) for each variable in probabilities.
data.table(prueba_anormalidad1$releffects,keep.rownames = T)%>%
#mutate_at(vars(-rn),~scales::percent(.))%>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Table 5. Non-parametric treaatment effects for each variable",
align =rep('c', 101),
col.names=c("Levels of Biopsychosocial Compromise","Std.Avg.Dose", "Std.Avg.Drug.Cons.", "Std.Avg.Health", "Std.Avg.Work & Education", "Robbery","Theft", "Drug-Selling","Fights", "Other Transgressions", "Stable Place to Live", "Basic Housing Cond.", "Dom. Violence"))%>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8)%>%
kableExtra::scroll_box(width = "100%", height = "225px")| Levels of Biopsychosocial Compromise | Std.Avg.Dose | Std.Avg.Drug.Cons. | Std.Avg.Health | Std.Avg.Work & Education | Robbery | Theft | Drug-Selling | Fights | Other Transgressions | Stable Place to Live | Basic Housing Cond. | Dom. Violence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1-Leve | 0.42667 | 0.36441 | 0.62646 | 0.62207 | 0.45544 | 0.47374 | 0.48237 | 0.45428 | 0.49816 | 0.52768 | 0.52802 | 0.46333 |
| 2-Moderado | 0.49507 | 0.47526 | 0.52688 | 0.53383 | 0.49082 | 0.49072 | 0.49321 | 0.49211 | 0.49790 | 0.51852 | 0.51753 | 0.50058 |
| 3-Severo | 0.53752 | 0.59716 | 0.40261 | 0.39199 | 0.53380 | 0.52679 | 0.51898 | 0.53195 | 0.50445 | 0.45622 | 0.45785 | 0.51335 |
The can see that in average, there was a greater probability that the group of users with a severe biopsychosocial compromise shows a greater percentage of each component of Treatment Outcomes Profile than a randomly chosen group, excepting those components related to health & social functioning.
CONS_C1_df_dup_JUL_2020_match_top_sel2<-
CONS_C1_df_dup_JUL_2020_match_top_sel%>%
dplyr::mutate_at(vars(hurto, robo, venta_drogas, rina, otro, lugar_vivir, vivienda,vif), ~as.numeric(as.factor(as.numeric(.)-1)))
#https://www.rdocumentation.org/packages/vegan/versions/2.4-2/topics/adonis
#t looks like the jaccard distance is really only useful for binary data (presence/absence) while The bray-curtis matrix has been found to be robust for many abundance type data sets, especially those with many paired zeros like stomach content data can have.
# Test of significance of the canonical relationship:
# 6.3. Canonical correspondence analysis (CCA)
# Beware: there are cca functions in other R packages, in particular in ade4.
#http://jinliang.weebly.com/uploads/2/5/7/8/25781074/analysis_of_community_ecology_data_in_r.pdf
#
#Statistical testing of microbial beta diversity between treatments was conducted in R using the vegan R package ADONIS function with the default 999 permutations. The betadisper function from the vegan R package was used to analyze variance between groups. Differential abundance analysis of the OTUs in microcosms of different treatments was performed using the DESeq2 R package. The DESeq2 generated the log fold change values, normalized relative abundances (“basemean”), and p-values from the Wald test with local fitting.
#ADONIS results indicate the effect of each factor, substrate, concentration and timepoint, on
#microbial community dissimilarity
vegan_1<-
vegan::adonis(CONS_C1_df_dup_JUL_2020_match_top_sel2[,c("dosis_mean_z", "total_mean_z", "salud_mean_z","total_trabajo_edu_z", "hurto", "robo", "venta_drogas", "rina", "otro", "lugar_vivir", "vivienda", "vif"),drop = FALSE] ~ compromiso_biopsicosocial, data=CONS_C1_df_dup_JUL_2020_match_top_sel,permutations=1000, parallel = 4, na.rm=T)
vegan_2<-
vegan::adonis(CONS_C1_df_dup_JUL_2020_match_top_sel2[,c("dosis_mean_z", "total_mean_z", "salud_mean_z","total_trabajo_edu_z", "hurto", "robo", "venta_drogas", "rina", "otro", "lugar_vivir", "vivienda", "vif"),drop = FALSE] ~ compromiso_biopsicosocial, data=CONS_C1_df_dup_JUL_2020_match_top_sel,permutations=1000, parallel = 4, na.rm=T, method="gower")
#https://trace.tennessee.edu/cgi/viewcontent.cgi?article=5817&context=utk_graddiss
#v To test for differences in dispersion between groups (permutational multivariate analysis of dispersion [PERMDISP] was significant), we used the betadisper function in vegan
#(PERMANOVA pseudo-F = 3.95, p = .0001), and all pairwise comparisons were significant (p < .001, 999 permutations). The PERMDISP was significant (F = 11.47, p = .0003), indicating unequal variances among vegetation habitats.
# To test for differences in dispersion between groups (permutational multivariate analysis of dispersion [PERMDISP] was significant), we used the betadisper function in vegan
#to determine if there were significant differences in MDS clusters by sample origin, an analysis of variance was conducted using the function “adonis” in the R package “vegan”.
sum_df_vegan<-data.frame(cbind(Model=paste0("vegan",c("Raw","Raw","Raw")),
rbind(data.table::data.table(vegan_1$ aov.tab, keep.rownames=T))))%>%
dplyr::rename("Parameter"="rn")%>%
dplyr::mutate(`Pr..F.`=sprintf("%5.3f",`Pr..F.`))
sum_df_vegan%>%
dplyr::mutate(R2=round(as.numeric(as.character(R2)),3))%>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Table 6. Analysis of Variance using bray-curtis matrix",
align =rep('c', 101),
col.names=c("Model","Parameter", "Df","SS", "Mean Squares","Pseudo F","R2", "p-value"))%>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::add_footnote( c("Note. Model with average dose (z score), average frequency of use (z score), average reported health (z score), average days worked or attending an educational institution (z score), theft, burglary, drug selling, fights, other, place to live, housing condition and domestic violence."), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| Model | Parameter | Df | SS | Mean Squares | Pseudo F | R2 | p-value |
|---|---|---|---|---|---|---|---|
| veganRaw | compromiso_biopsicosocial | 2 | 15.07365 | 7.5368228 | 207.1233 | 0.064 | 0.001 |
| veganRaw | Residuals | 6,031 | 219.45665 | 0.0363881 | NA | 0.936 | NA |
| veganRaw | Total | 6,033 | 234.53029 | NA | NA | 1.000 | NA |
| Note. Model with average dose (z score), average frequency of use (z score), average reported health (z score), average days worked or attending an educational institution (z score), theft, burglary, drug selling, fights, other, place to live, housing condition and domestic violence. |
get_adjusted_r2 <- function(adonis_object) {
n_observations <- ncol(adonis_object$coef.sites)
d_freedom <- adonis_object$aov.tab$Df[1]
r2 <- adonis_object$aov.tab$R2[1]
adjusted_r2 <- vegan::RsquareAdj(r2, n_observations, d_freedom)
adjusted_r2
}
#dg_cons_dep", "sin_otros_prob_sm","abandono_temp",,"dg_cie_10"
#https://stackoverflow.com/questions/16638297/vegdist-error-message-in-vegan-packageDespite both tests indicated significant differences between the levels of biopsychosocial compromise in scores of the TOP, the r-square let us interpret that these differences only explain the 6% of the variances of the scores, in such way that levels in the biopsychosocial compromise may not be capable of predicting a determin, affecting the generalizab
Relationship with outcomes
library(glmulti)
#sin_otros_prob_sm+ dg_cons_dep
res <- glmulti(abandono_temp ~ compromiso_biopsicosocial + origen_ingreso_mod + dg_cie_10_rec + dg_trs_fisico, #tal vez no debería meter esta última variable.
data=CONS_C1_df_dup_JUL_2020_match_top_sel2%>%dplyr::mutate(compromiso_biopsicosocial=case_when(compromiso_biopsicosocial=="2-Moderado"~"Moderate",compromiso_biopsicosocial=="1-Leve"~"Mild",TRUE~"Severe")),
level=2, #The level of interaction between explanatory variables to be considered. Currently only 1 (only main effects) or 2 (main effects plus all pairwise interactions) are supported.
fitfunction=glm, crit="aicc",
family=binomial,
marginality=T,#Whether to use the general marginality rule or not. Default is FALSE. With TRUE, all predictors found in interaction terms are also included as main effects.
confsetsize = 1000 #How many models should be returned in the confidence set of models?
)Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...
After 50 models:
Best model: abandono_temp~1+origen_ingreso_mod+dg_cie_10_rec+dg_trs_fisico
Crit= 5556.33253408953
Mean crit= 5709.57787753471
After 100 models:
Best model: abandono_temp~1+origen_ingreso_mod+dg_cie_10_rec+dg_trs_fisico+compromiso_biopsicosocial+dg_cie_10_rec:compromiso_biopsicosocial
Crit= 5555.50391620977
Mean crit= 5636.49090121402Completed.#t is not iterative (all models are compared), and is fully automatic (no intervention of the user is required). It is primarily intended to be used for exploratory analyses with many candidate explanatory variables (say 10 to 20)
plot(res)##########################################################################################
##################DEBIESE INCORPORAR EL TIPO DE PLAN!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
##########################################################################################
#http://www.metafor-project.org/doku.php/tips:model_selection_with_glmulti_and_mumin
#But let's take a look at the top 10 models:
top <- weightable(res) #Prepares a table with model formulas, IC values and IC relative supports
top <- top[top$aicc <= min(top$aicc) + 5,]
############################################################################################
###########################################################################################
plot.glmulti<-function(x, type="p", ...)
{
if ( type == "p") {
plot(x@crits,xlab="Best models",
ylab=paste("Support (",x@params$crit,")"),pch=19, main="IC profile", ...)
abline(h=x@crits[1]+2,col="red")
return(assign("ic_glmulti",data.table::data.table(data.frame(imp),keep.rownames = T),envir = .GlobalEnv))
} else if (type == "w") {
ww = exp(-(x@crits - x@crits[1])/2)
ww=ww/sum(ww)
plot(ww,xlab="Best models",
ylab=paste("Evidence weight (",x@params$crit,")"),pch=19, main="Profile of model weights", ...)
cucu=function(i) sum(ww[1:i])
wwc=lapply(1:length(ww),cucu)
abline(v=min(which(wwc>=0.95)),col="red")
} else if (type=="r") {
if (length(x@objects)) {
# shows some diagnostics of the fit
dev.new()
par(mfrow=c(2,min(length(x@crits), 5)))
for (k in 1:min(length(x@crits), 5))
plot(x@objects[[k]],which=c(1), main=deparse(x@formulas[[k]]),...)
for (k in 1:min(length(x@crits), 5))
plot(x@objects[[k]],which=c(2),...)
} else warning("Unavailable: use includeobjects=T when calling glmulti.")
} else if (type=="s") {
# plots variable (i.e. terms) importances
ww = exp(-(x@crits - x@crits[1])/2)
ww=ww/sum(ww)
# handle synonymies for interactions
# this translates to unique notations (e.g. x:y and y:x are the same)
clartou=function(x) {
sort(strsplit(x, ":")[[1]])-> pieces
if (length(pieces)>1) paste(pieces[1],":",pieces[2],sep="")
else x
}
# list terms in models
tet = lapply(x@formulas, function(x) sapply(attr(delete.response(terms(x)),"term.labels"), clartou))
# all unique terms
unique(unlist(tet))-> allt
# importances
sapply(allt, function(x) sum(ww[sapply(tet, function(t) x%in%t)]))-> imp
# draw
#par(oma=c(0,13,0,0))
barplot(sort(imp),xlab="",xlim=c(0,1), ylab="",horiz=T,las=2, names.arg=allt[order(imp)],main="Model-averaged importance of terms", ...)
abline(v=0.8, col="red")
return(assign("var_imp_glmulti",data.table::data.table(data.frame(imp),keep.rownames = T),envir = .GlobalEnv))
} else warning("plot: Invalid type argument for plotting glmulti objects.")
}
plot(res, type="s")############################################################################################data.frame(res@crits)%>% dplyr::mutate(rn=row_number())%>%
ggplot(aes(x=rn,y=res.crits))+
geom_point(color="steelblue")+
sjPlot::theme_sjplot2() +
ylab("Support (aicc)")+
theme(axis.text.x = element_text(size=8,vjust = .5,angle=25), axis.text.y = element_text(size=10),
axis.title.y = element_text(size = 14)) +
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
line = element_blank())+
geom_hline(yintercept=res@crits[1]+2, color="red", linetype=2,size=1)+
xlab("Best models")
Figure 7. Ordered Models and AICC
We selected the model with lower AIC. The first model contained shown the more parsimonius indices (AICc= 5555.46, IC Rel. Support= 0.104).
#par(oma=c(0,0,0,24))
ggplot(var_imp_glmulti, aes(x=reorder(rn, -imp),y=imp))+
geom_bar(stat="identity",alpha=.7, fill="steelblue")+
sjPlot::theme_sjplot2() +
ylab("Model-averaged importance")+
theme(axis.text.x = element_text(size=6.5,vjust = .5,angle=25),
axis.text.y = element_text(size=10),
axis.title.y = element_text(size = 14)) +
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
line = element_blank())+
geom_hline(yintercept=.5, color="red", linetype=2)+
xlab("Terms")
Figure 8. Model-averaged variable importance of terms
The average importance of the term biopsychosocial compromise had the third major importance over the outcome variable (early drop-out) among the 1,000 models tested.
df_coef_glmulti<- data.frame()
for (i in 1:nrow(summary(res@objects[[1]])$coefficients)){
df<-cbind(attr(summary(res@objects[[1]])$coefficients,"dimnames")[[1]][i],
t(data.frame(exp(summary(res@objects[[1]])$coefficients[i,1] +
+ qnorm(c(0.025,.5,0.975)) * summary(res@objects[[1]])$coefficients[i,2]))))
df_coef_glmulti<-rbind(df_coef_glmulti,df)
}
df_coef_glmulti<-data.table::data.table(df_coef_glmulti)
data.table::data.table(summary(res@objects[[2]])$coefficients,keep.rownames = T)%>%
dplyr::left_join(df_coef_glmulti,by=c("rn"="V1"))%>%
dplyr::select(-V3)%>%
dplyr::mutate(V2=round(as.numeric(as.character(V2)),2))%>%
dplyr::mutate(V4=round(as.numeric(as.character(V4)),2))%>%
dplyr::mutate(Estimate=round(as.numeric(as.character(Estimate)),2))%>%
dplyr::mutate(OR=round(exp(as.numeric(as.character(Estimate))),2))%>%
#exp(summary(m)$coefficients["DSH",1] +
#+ qnorm(c(0.025,0.5,0.975)) * summary(m)$coefficients["DSH",2])
#[1] 1.472098 4.197368 11.967884
dplyr::mutate(`Std. Error`=round(as.numeric(as.character(`Std. Error`)),2))%>%
dplyr::mutate(`z value`=round(as.numeric(as.character(`z value`)),2))%>%
dplyr::mutate(`Pr(>|z|)`=round(as.numeric(as.character(`Pr(>|z|)`)),4))%>%
dplyr::rename("Terms"="rn", "OR_lo"="V2","OR_up"="V4")%>%
dplyr::select(Terms,Estimate,OR,OR_lo,OR_up, everything())%>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Table 7. Logistic Regression Coefficients",
align =rep('c', 101))%>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8)%>%
kableExtra::add_footnote( c(paste("Note. AICc=",round(res@objects[[1]]$aic,3)),
paste("Defined model=",top$model[1])), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| Terms | Estimate | OR | OR_lo | OR_up | Std. Error | z value | Pr(>|z|) |
|---|---|---|---|---|---|---|---|
| (Intercept) | -2.08 | 0.12 | 0.07 | 0.16 | 0.21 | -9.99 | 0.0000 |
| origen_ingreso_modDerivación Asistida | -0.22 | 0.80 | 0.63 | 1.02 | 0.12 | -1.82 | 0.0690 |
| origen_ingreso_modOtros | -0.62 | 0.54 | 0.37 | 0.77 | 0.19 | -3.32 | 0.0009 |
| origen_ingreso_modSector Justicia | -0.42 | 0.66 | 0.51 | 0.87 | 0.14 | -3.06 | 0.0022 |
| origen_ingreso_modSector Salud | -0.39 | 0.68 | 0.57 | 0.79 | 0.08 | -4.74 | 0.0000 |
| dg_cie_10_rec1 | 1.59 | 4.90 | 3.00 | 7.33 | 0.23 | 6.86 | 0.0000 |
| dg_trs_fisico1 | -0.72 | 0.49 | 0.58 | 0.77 | 0.22 | -3.26 | 0.0011 |
| compromiso_biopsicosocialModerate | -0.04 | 0.96 | 0.72 | 1.64 | 0.24 | -0.18 | 0.8582 |
| compromiso_biopsicosocialSevere | 0.30 | 1.35 | 1.09 | 2.75 | 0.27 | 1.14 | 0.2527 |
| dg_cie_10_rec1:compromiso_biopsicosocialModerate | 0.02 | 1.02 | 0.65 | 1.76 | 0.26 | 0.09 | 0.9306 |
| dg_cie_10_rec1:compromiso_biopsicosocialSevere | -0.45 | 0.64 | 0.40 | 1.18 | 0.28 | -1.61 | 0.1076 |
| dg_trs_fisico1:compromiso_biopsicosocialModerate | 0.28 | 1.32 | NA | NA | 0.24 | 1.17 | 0.2436 |
| dg_trs_fisico1:compromiso_biopsicosocialSevere | 0.48 | 1.62 | NA | NA | 0.26 | 1.88 | 0.0596 |
| Note. AICc= 5555.46 | |||||||
| Defined model= abandono_temp ~ 1 + origen_ingreso_mod + dg_cie_10_rec + dg_trs_fisico + compromiso_biopsicosocial + dg_cie_10_rec:compromiso_biopsicosocial |
#abandono_temp ~ 1 + compromiso_biopsicosocial + origen_ingreso_mod + dg_cie_10 + dg_trs_fisico + dg_trs_fisico:compromiso_biopsicosocial
#print(res@formulas[[2]])The next figures show the marginal effects of the interaction terms among the variables.
library(lsmeans)
refR_1 <- lsmeans(res@objects[[1]], specs = c("compromiso_biopsicosocial","dg_trs_fisico"))
ref_dfR_1 <- as.data.frame(cbind(summary(refR_1)[1:2],exp(summary(refR_1)[,c(3,6,7)])))
g4R_1 <- ggplot(ref_dfR_1, aes(x=compromiso_biopsicosocial, y=lsmean,group=dg_trs_fisico, colour=dg_trs_fisico))+
geom_errorbar(aes(ymin=asymp.LCL, ymax=asymp.UCL), width=.1,position=position_dodge(0.1), size=1) +
geom_line(position=position_dodge(0.1), size=.5)+
geom_point(position=position_dodge(0.1), size=2)+
xlab("Biopsychosocial Compromise")+
ylab("Odds Ratio (OR) of Early Drop-out")+
sjPlot::theme_sjplot2() +
geom_rect_interactive(alpha = 0.1, xmin=.1, xmax=.1, ymin=.1,ymax=.1) +
# Remove plot elements added by geom_rect_interactive
theme(legend.position="bottom")+
guides(color=guide_legend(ncol=4))+
labs(color="Cause of Discharge")+
scale_colour_brewer(name="Cumulative Percentage", palette = "Set1",labels=c("No Dg.", "In study or Dg."))+
theme(legend.title = element_blank())
#theme(axis.text = element_blank(), axis.ticks = element_blank())
ggiraph(code = print(g4R_1))Figure 9. Interactive Effects in the Prob. of Early Drop-out with Diagnose of Physical Diagnosis
library(lsmeans)
refR_2 <- lsmeans(res@objects[[1]], specs = c("compromiso_biopsicosocial","dg_cie_10_rec"))
ref_dfR_2 <- as.data.frame(cbind(summary(refR_2)[1:2],exp(summary(refR_2)[,c(3,6,7)])))
g4R_2 <- ggplot(ref_dfR_2, aes(x=compromiso_biopsicosocial, y=lsmean,group=dg_cie_10_rec, colour=dg_cie_10_rec))+
geom_errorbar(aes(ymin=asymp.LCL, ymax=asymp.UCL), width=.1,position=position_dodge(0.1), size=1) +
geom_line(position=position_dodge(0.1), size=.5)+
geom_point(position=position_dodge(0.1), size=2)+
xlab("Psychiatric Condition")+
ylab("Odds Ratio (OR) of Early Drop-out")+
sjPlot::theme_sjplot2() +
geom_rect_interactive(alpha = 0.1, xmin=.1, xmax=.1, ymin=.1,ymax=.1) +
# Remove plot elements added by geom_rect_interactive
theme(legend.position="bottom")+
guides(color=guide_legend(ncol=4,name = "Cause of Discharge"))+
labs(color="Motive of Admission to Treatment")+
scale_colour_brewer(palette = "Set1",labels=c("No Dg.", "In study or Dg."))+#,labels=c("No Dg.", "In study", "Dg."))+
theme(legend.title = element_blank())
#theme(axis.text = element_blank(), axis.ticks = element_blank())
ggiraph(code = print(g4R_2))Figure 10. Interactive Effects in the Prob. of Early Drop-out with Diagnose of Psychiatric Diagnosis
save.image("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/biopsychosoc_comp.RData")
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
[3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
[5] LC_TIME=Spanish_Chile.1252
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gdtools_0.2.2 glmulti_1.0.8 leaps_3.1
[4] rJava_0.9-12 reshape2_1.4.4 polycor_0.7-10
[7] DiagrammeR_1.0.6.1.9000 compareGroups_4.4.5 MVN_5.8
[10] citr_0.3.2 ggcorrplot_0.1.3 psych_2.0.7
[13] ggiraph_0.7.0 finalfit_1.0.1 lsmeans_2.30-0
[16] emmeans_1.4.7 choroplethrAdmin1_1.1.1 choroplethrMaps_1.0.1
[19] choroplethr_3.6.3 acs_2.1.4 XML_3.99-0.3
[22] RColorBrewer_1.1-2 panelr_0.7.3 lme4_1.1-23
[25] Matrix_1.2-18 dplyr_1.0.0 data.table_1.12.8
[28] codebook_0.9.2 Statamarkdown_0.4.5 devtools_2.3.0
[31] usethis_1.6.1 sqldf_0.4-11 RSQLite_2.2.0
[34] gsubfn_0.7 proto_1.0.0 broom_0.7.0
[37] zoo_1.8-8 altair_4.0.1 rbokeh_0.5.1
[40] janitor_2.0.1 plotly_4.9.2.1 kableExtra_1.1.0
[43] Hmisc_4.4-0 Formula_1.2-3 survival_3.1-12
[46] lattice_0.20-41 ggplot2_3.3.1 stringr_1.4.0
[49] stringi_1.4.6 tidyr_1.1.0 knitr_1.29
[52] matrixStats_0.56.0 boot_1.3-25
loaded via a namespace (and not attached):
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[4] lmtest_0.9-37 rprojroot_1.3-2 crayon_1.3.4
[7] laeken_0.5.1 MASS_7.3-51.6 nlme_3.1-148
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[13] performance_0.4.6 nloptr_1.2.2.1 callr_3.4.3
[16] flextable_0.5.10 rjson_0.2.20 ggmap_3.0.0
[19] bit64_0.9-7 glue_1.4.1 sjPlot_2.8.4
[22] processx_3.4.2 classInt_0.4-3 tcltk_4.0.2
[25] haven_2.3.1 tidyselect_1.1.0 NADA_1.6-1.1
[28] rio_0.5.16 sf_0.9-3 sjmisc_2.8.5
[31] chron_2.3-55 xtable_1.8-4 magrittr_1.5
[34] evaluate_0.14 RgoogleMaps_1.4.5.3 cli_2.0.2
[37] rstudioapi_0.11 miniUI_0.1.1.1 sp_1.4-2
[40] robCompositions_2.2.1 rpart_4.1-15 jtools_2.0.5
[43] pls_2.7-2 zCompositions_1.3.4 sjlabelled_1.1.5
[46] RJSONIO_1.3-1.4 maps_3.3.0 shiny_1.5.0
[49] gistr_0.5.0 xfun_0.16 parameters_0.7.0
[52] pkgbuild_1.1.0 cluster_2.1.0 sgeostat_1.0-27
[55] tibble_3.0.1 permute_0.9-5 png_0.1-7
[58] reshape_0.8.8 withr_2.2.0 bitops_1.0-6
[61] ranger_0.12.1 plyr_1.8.6 cellranger_1.1.0
[64] pcaPP_1.9-73 e1071_1.7-3 coda_0.19-3
[67] pillar_1.4.6 mvoutlier_2.0.9 multcomp_1.4-13
[70] fs_1.4.1 flexmix_2.3-15 kernlab_0.9-29
[73] vctrs_0.3.1 ellipsis_0.3.1 generics_0.0.2
[76] nortest_1.0-4 rgdal_1.5-8 tools_4.0.2
[79] foreign_0.8-80 munsell_0.5.0 fastmap_1.0.1
[82] compiler_4.0.2 pkgload_1.1.0 abind_1.4-5
[85] httpuv_1.5.4 tigris_0.9.4 npmv_2.4.0
[88] sessioninfo_1.1.1 gridExtra_2.3 visNetwork_2.0.9
[91] later_1.1.0.1 jsonlite_1.6.1 WDI_2.6.0
[94] GGally_1.5.0 scales_1.1.1 carData_3.0-4
[97] estimability_1.3 lazyeval_0.2.2 promises_1.1.1
[100] car_3.0-8 latticeExtra_0.6-29 reticulate_1.16
[103] effectsize_0.3.1 checkmate_2.0.0 rmarkdown_2.3
[106] openxlsx_4.1.5 sandwich_2.5-1 moments_0.14
[109] statmod_1.4.34 webshot_0.5.2 forcats_0.5.0
[112] pander_0.6.3 yaml_2.2.1 systemfonts_0.2.3
[115] prabclus_2.3-2 htmltools_0.5.0 memoise_1.1.0
[118] modeltools_0.2-23 viridisLite_0.3.0 digest_0.6.25
[121] rrcov_1.5-2 assertthat_0.2.1 mime_0.9
[124] rappdirs_0.3.1 repr_1.1.0 bayestestR_0.6.0
[127] units_0.6-6 remotes_2.1.1 vegan_2.5-6
[130] blob_1.2.1 splines_4.0.2 labeling_0.3
[133] fpc_2.2-7 cvTools_0.3.2 hms_0.5.3
[136] modelr_0.1.8 colorspace_1.4-1 base64enc_0.1-3
[139] mnormt_2.0.0 tmvnsim_1.0-2 nnet_7.3-14
[142] Rcpp_1.0.4.6 mclust_5.4.6 mvtnorm_1.1-1
[145] fansi_0.4.1 VIM_6.0.0 truncnorm_1.0-8
[148] R6_2.4.1 grid_4.0.2 lifecycle_0.2.0
[151] acepack_1.4.1 labelled_2.5.0 zip_2.0.4
[154] writexl_1.3 curl_4.3 pryr_0.1.4
[157] minqa_1.2.4 testthat_2.3.2 snakecase_0.11.0
[160] robustbase_0.93-6 skimr_2.1.1 desc_1.2.0
[163] TH.data_1.0-10 htmlwidgets_1.5.1 officer_0.3.11
[166] purrr_0.3.4 crosstalk_1.1.0.1 mgcv_1.8-31
[169] rvest_0.3.5 insight_0.8.4 htmlTable_2.0.1
[172] codetools_0.2-16 lubridate_1.7.9 prettyunits_1.1.1
[175] vegawidget_0.3.1 gtable_0.3.0 DBI_1.1.0
[178] stats4_4.0.2 httr_1.4.2 highr_0.8
[181] KernSmooth_2.23-17 farver_2.0.3 uuid_0.1-4
[184] diptest_0.75-7 hexbin_1.28.1 mice_3.9.0
[187] xml2_1.3.2 ggeffects_0.14.3 readr_1.3.1
[190] sROC_0.1-2 energy_1.7-7 DEoptimR_1.0-8
[193] bit_1.1-15.2 sjstats_0.18.0 jpeg_0.1-8.1
[196] pkgconfig_2.0.3 maptools_1.0-1 HardyWeinberg_1.6.6
[199] Rsolnp_1.16 References
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